Metaheuristics for portfolio optimization : an introduction using MATLAB® / G. A. Vijayalakshmi Pai.

Format:

Book

Author/Creator:

Pai, G. A. Vijayalakshmi, author.

Series:

Computer engineering series (London, England). Metaheuristics set ; Volume 11.

Computer Engineering Series : Metaheuristics Set ; Volume 11

Language:

English

Subjects (All):

Portfolio management--Mathematics.

Portfolio management.

Mathematical optimization.

Physical Description:

1 online resource (321 pages).

Edition:

1st ed.

Place of Publication:

London, England ; Hoboken, New Jersey : ISTE : Wiley, 2018.

Summary:

The book is a monograph in the cross disciplinary area of Computational Intelligence in Finance and elucidates a collection of practical and strategic Portfolio Optimization models in Finance, that employ Metaheuristics for their effective solutions and demonstrates the results using MATLAB implementations, over live portfolios invested across global stock universes. The book has been structured in such a way that, even novices in finance or metaheuristics should be able to comprehend and work on the hybrid models discussed in the book.

Contents:

Cover

Half-Title Page

Title Page

Copyright Page

Contents

Preface

PART 1

1. Introduction to Portfolio Optimization

1.1. Fundamentals of portfolio optimization

1.2. An example case study

1.2.1. Portfolio P: Unconstrained portfolio optimization

1.2.2. Portfolio P: Constrained portfolio optimization

1.2.3. Portfolio P: Sharpe Ratio-based portfolio optimization

1.3. MATLAB® demonstrations

2. A Brief Primer on Metaheuristics

2.1. Metaheuristics framework

2.2. Exact methods versus metaheuristics

2.3. Population-based metaheuristics - Evolutionary Algorithms

2.3.1. Evolutionary Algorithm basics

2.4. Evolution Strategy

2.4.1. Evolution Strategy: genetic inheritance operators

2.4.2. Evolution Strategy process flow chart

2.4.3. Demonstration of Evolution Strategy

2.4.4. Experimental results and analysis

2.5. Differential Evolution strategy

2.5.1. Differential Evolution operators

2.5.2. Differential Evolution strategy process flow chart

2.5.3. Differential Evolution strategies

2.5.4. Demonstration of Differential Evolution Strategy

2.5.5. Experimental results and analysis

2.6. MATLAB® demonstrations

PART 2

3. Heuristic Portfolio Selection

3.1. Portfolio selection

3.2. Clustering

3.3. k-means clustering

3.4. Heuristic selection of securities

3.4.1. Heuristic portfolio selection for S&amp

P BSE200

3.4.2. Heuristic portfolio selection for Nikkei225

3.5. k-portfolio performance

3.5.1. Equal Weighted k-portfolio construction

3.5.2. Inverse Volatility Weighted k-portfolio construction

3.5.3. Other k-portfolio characteristics

3.6. MATLAB® demonstrations

4. Metaheuristic Risk-Budgeted Portfolio Optimization

4.1. Risk budgeting

4.2. Long-Short portfolio

4.3. Risk-Budgeted Portfolio Optimization model.

4.3.1. Constraint handling

4.3.2. Transformed Risk-budgeted portfolio optimization model

4.4. Differential Evolution with Hall of Fame

4.5. Repair strategy for handling unbounded linear constraints

4.6. DE HOF-based Risk-budgeted portfolio optimization

4.7. Case study global portfolio: results and analyses

4.7.1. Finding the optimal Risk-budgeted portfolio using DE HOF

4.7.2. Consistency of performance of DE HOF

4.7.3. Convergence characteristics of DE HOF

4.8. MATLAB® demonstrations

5. Heuristic Optimization of Equity Market Neutral Portfolios

5.1. Market neutral portfolio

5.2. Optimizing a naïve equity market neutral portfolio

5.3. Risk-budgeted equity market neutral portfolio

5.4. Metaheuristic risk-budgeted equity market neutral portfolios

5.4.1. Rand5/Dir4 strategy

5.4.2. Tournament selection

5.4.3. Constraint handling

5.5. Experimental results and analyses

5.6. MATLAB® demonstrations

6. Metaheuristic 130-30 Portfolio Construction

6.1. 130-30 portfolio

6.2. 130-30 portfolio optimization: mathematical formulation

6.3. 130-30 portfolio optimization using MATLAB Financial Toolbox™

6.3.1. Experimental results

6.4. Metaheuristic 130-30 portfolio optimization

6.4.1. Transformation of 130-30 portfolio optimization model

6.4.2. Constraint handling

6.4.3. Differential Evolution-based 130-30 portfolio construction

6.4.4. Experimental results

6.5. MATLAB® demonstrations

7. Metaheuristic Portfolio Rebalancing with Transaction Costs

7.1. Portfolio rebalancing

7.2. Portfolio rebalancing mathematical model

7.3. Evolution Strategy with Hall of Fame for Portfolio Rebalancing

7.3.1. Evolution Strategy - a brief note

7.3.2. Optimal rebalanced portfolio using ES HOF

7.3.3. Weight repair strategy for portfolio rebalancing

7.4. Experimental results.

7.4.1. ES HOF-based rebalancing of a high risk S&amp

P BSE200 portfolio

7.4.2. Convergence characteristics of ES HOF

7.4.3. Consistency of performance of ES HOF

7.5. Comparison of Non-Rebalanced and Rebalanced portfolios

7.5.1. Weight drift analysis for the Non-Rebalanced portfolio

7.5.2. Non-Rebalanced versus Rebalanced portfolios - which of these is better?

7.5.3. Risk-Return performance of the Rebalanced and Non-Rebalanced portfolios

7.6. MATLAB® demonstrations

Conclusion

Bibliography

Index

Other titles from iSTE in Computer Engineering

EULA.

Notes:

Includes bibliographical references and index.

Description based on print version record.

ISBN:

9781119482796

1119482798

9781119482789

111948278X

9781119482840

1119482844

OCLC:

1019654665